276 Int. J. Renewable Energy Technology, Vol. 6, No. 3, 2015

Copyright © 2015 Inderscience Enterprises Ltd.

Smoothing wind power fluctuations by artificial neural network-based pitch angle controller

M.A. Chowdhury Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Jalan SimpangTiga, 93350 Kuching, Sarawak, Malaysia Email: mchowdhury@swinburne.edu.my

Abstract: Wind energy has been receiving more acceptance as a reproducible, resourceful and clean energy source since last decade. Wind power is not constant and may fluctuate below the rated wind power when the wind speed is lower than the rated speed. This fact affects the stability of the power system, to which the wind generators are connected. This is becoming more significant with the increasing penetration of wind energy systems. Pitch angle control has been one of the most common methods for smoothing output power fluctuations during below rated wind incidents. An artificial neural network (ANN)-based pitch angle controller is proposed in this paper for smoothing wind power fluctuations during below rated wind incidents beside traditional power regulations during above rated wind incidents. Two smoothing methods have been presented: the determination of the command output power based on the exponential moving average with a proper selection of correction factor by neural network and the dynamic selection of target output power according to the wind incident. Simulation results show the effectiveness of the proposed ANN-based pitch angle controller in smoothing output power fluctuations with significantly small drop of output power.

Keywords: wind power; smoothing power fluctuations; pitch angle control; artificial neural network; ANN.

Reference to this paper should be made as follows: Chowdhury, M.A. (2015) ‘Smoothing wind power fluctuations by artificial neural network-based pitch angle controller’, Int. J. Renewable Energy Technology, Vol. 6, No. 3, pp.276–294.

Biographical notes: Md. Ayaz Chowdhury obtained his Bachelor degree in Electrical and Electronic Engineering from Islamic University of Technology (IUT), Bangladesh, in 2007. He worked from 2008 as a Lecturer with International Islamic University Chittagong (IIUC), Bangladesh. He finished his PhD from Swinburne University of Technology, Australia in 2013. Currently, he is working as a Lecturer at Swinburne University of Technology Sarawak Campus. His research is mainly focused on power system stability and control, grid integration of renewable energy and smart grid.

1 Introduction

In recent years, wind energy has become popular due to its inherent attribute of reproducible, resourceful and pollution-free characteristics against the rapid depletion and

Smoothing wind power fluctuations by ANN-based pitch angle controller 277

increasing environmental threats of conventional energy. Moreover, wind energy has been competing with conventional energy as a result of its cost reduction with technological advancements and incentives for adopting renewable energy since last decade. All these factors have caused wind power to become the fastest growing energy source.

In this situation, many small scale wind farms are connected to the distribution network while many wind farms of 50 MW or more are directly connected to transmission network (Lin et al., 2009). These bring new challenges to the stability of power system. One of the challenges is that wind power is not constant and can fluctuate significantly below the rated power since wind power is proportional to the cube of the wind speed. The problems originated by the wind power fluctuations are as follows (Kamel et al., 2010):

• Wind power fluctuations may cause the grid frequency to fluctuate.

• Amount of absorbed reactive power by the induction generator from power grid is directly related to the active power generation. The variation in wind speed causes the fluctuation of the active power generation and thus the absorbed reactive power, leading to voltage flicker at the buses of the power grid.

• Frequency fluctuation and voltage flicker provide poor power quality and originate instability problems in the power system, especially when there are loads sensitive to accept high voltage and frequency variations.

The importance of smoothing output power fluctuations (SOPF) becomes more significant with the increasing penetration of wind energy systems into the grid. The synchronisation phenomena of wind turbines in a wind farm have been discussed (Cidras et al., 2002). It showed that a wind farm with many wind turbines has the natural tendency of the SOPF, however this tendency may be lost if the output power fluctuation is synchronised from the synchronisation phenomena. Recently, the provision of power storage system for the SOPF has been proposed (Kamel et al., 2010; Kinjo et al., 2006; Senjyu et al., 2004; Muyeen et al., 2008, 2012; Islam et al., 2013). This strategy is effective when power quality is concerned for high sensitive loads, but it is not efficient from economic point of view.

On the other hand, pitch angle control has become a very popular method for the SOPF, which can be achieved by different methods, like minimum variance control (Sakamoto et al., 2004), H∞ control (Sakamoto et al., 2006), fuzzy logic control (Kamel et al., 2010), generalised predictive control (Senjyu et al., 2006) to ensure that the generated output power follows a command value determined by exponential moving average (EMA). Although these methods provided a reliable smoothing operation at low cost, they can only perform partial SOPF and hence offer the partial solution to the problems originated by the output power fluctuations. This kept the compensation role of the power storage system for proper smoothing still significant. In addition, they caused a large drop in output power.

In this paper, an artificial neural network (ANN) based pitch angle controller is proposed on the motivation of better smoothing performance with a lesser drop in output power than that was achieved in the previous literature. ANN adapts the wind energy system’s nonlinearity and ambiguous dynamics with proper training (Taalab et al., 1999).

Two smoothing methods are presented. The first method combines the work in Kamel et al. (2010) and Senjyu et al. (2006), it determines the command output power through

278 M.A. Chowdhury

the EMA with a proper selection of correction factor by neural network so that the output power follows the command value by dynamic pitch actuation. It eases the ANN training for achieving proper input-output correlation with positive domain control input so that it gives optimum SOPF with minimum possible pitch angle generation. It results in lesser drop in output power. The second method selects target output power to which the output power would be limited to. Different values of a multiplication factor, n are assigned for different target values to enable such dynamic pitch actuation so that it ensures proper SOPF with lesser drop in output power. A dynamic simulation is carried out to validate the effectiveness of the proposed ANN-based pitch angle controller.

This paper is organised as follows. Section 2 presents the configuration of wind generation system with their characteristic equations. Section 3 describes the conventional proportional integral (PI) pitch angle controller. Section 4 presents the control strategy, structure and training method of the proposed ANN-based pitch angle controller. Section 5 presents the simulation results demonstrating the effectiveness of the proposed methods in SOPF, followed by the conclusions in Section 6.

2 Wind power generation system

The aerodynamics of the wind turbine is characterised by Cp-λ-β curve, which is usually provided by the manufacturers. Cp is the power coefficient; it corresponds to maximum mechanical power extraction from wind at its maximum value and is a function of the tip-speed ratio (λ) and the pitch angle (β). For a given Cp, the mechanical power (Pm) and mechanical torque (Tm) extracted from the wind by the wind turbine can be expressed by (Gracia et al., 2008):

3( , )2

p Wm

ρAC λ VP

β= (1)

mm

t

PTω

= (2)

where ρ is the air density, A is the sweep area of the blades, VW is the wind speed and ωt is the turbine rotor speed.

Conventionally, the rotor is treated as two lumped masses, i.e., turbine mass and generator mass. They are connected together by a shaft with a certain damping and stiffness coefficient values. Neglecting the turbine and generator self-damping, shaft stiffness and torsional oscillations, the mathematical equation can be expressed as (Gracia et al., 2008):

( )2 gt g m e

dωH H T T

dt+ = − (3)

where Ht is the turbine inertia constant, Hg is the generator inertia constant, ωg is the generator rotor speed and Te is the electromagnetic torque.

For the induction generator, a synchronously rotating d-q reference frame of fourth order is used, which is rotating with the same speed as the stator voltage. Stator and rotor voltages in this reference frame are given in the following equations (Gracia et al., 2008):

Smoothing wind power fluctuations by ANN-based pitch angle controller 279

1 dsds s ds qs

b

dv R i

ω dtφ

φ= − − + (4)

1 qsqs s qs ds

bv R i

ω dtφ

φ= − + + (5)

1 drdr r dr qr

b

dv R i s

ω dtφ

φ= − + (6)

1 qrqr r qr dr

b

dv R i s

ω dtφ

φ= + + (7)

where v is the voltage, i is the current, R is the resistance, s is the slip, φ is the flux and ωb is the angular frequency. Suffix s, r, d and q denote stator, rotor, d-axis component and q-axis component, respectively.

The electromagnetic torque (Te) is expressed as

( )e ds qs qs dsT p i iφ φ= − (8)

where p is the number of pole pairs. The output active power (Pg) and reactive power (Qg) of a wind turbine delivered to

the grid are calculated as

g ds ds qs qs dr dr qr qrP v i v i v i v i= − + − − (9)

g qs ds ds qs qr dr dr qrQ v i v i v i v i= + − − (10)

The parameters of the wind turbine used in this work are shown in Table 1 (SimPowerSystems, 2010). Table 1 Wind turbine parameters

Parameter Symbol Value Unit

Nominal mechanical output power Pmec 1.5 MW

Nominal electrical power Pelec 1.5/0.9 MW

Nominal voltage (L-L) Vnom 575 Volt

Stator resistance Rs 0.00706 p.u.

Stator leakage inductance Lr 0.171 p.u.

Rotor resistance Rr 0.0058 p.u.

Rotor leakage inductance Lr 0.156 p.u.

Magnetising inductance Lm 2.9 p.u.

Base frequency f 60 Hz

Inertia constant H 1 s

Friction factor F 0.01 p.u.

Pair of poles p 3 -

280 M.A. Chowdhury

3 Conventional PI pitch angle controller

The control system generates the voltage command signals for the rotor and grid side converters in order to control the DC voltage and the reactive power or the voltage at the grid terminals. The power converters must be controlled in collaboration with the pitch angle controller so that it forces wind turbines to follow a predefined power-speed characteristic, known as ‘tracking characteristic’ to ensure optimal energy capture when the mechanical power is extracted by the rotor during both below and above nominal wind incidents. Tracking characteristic curve follows the maximum power points for different wind speeds, which is illustrated by the red curve (Figure 1) (SimPowerSystems, 2010). Power reaches 1 p.u. at minimum generator rotor speed value of 1.21 p.u., which is held constant for any values higher than this.

Figure 1 Turbine power characteristics and tracking characteristic (see online version for colours)

Figure 2 Control scheme of the conventional PI pitch angle controller

Conventionally, PI pitch angle controller is used to maintain the output power of the wind turbine at its rated value by adjusting the pitch angle of the blades, which provides an effective means of regulations in strong wind speeds. Figure 2 shows a conventional simplified PI pitch angle controller, which regulates the output in accordance with the error (e) between generator rotor speed (ωg) and its upper limit (reference) value. This upper limit value is chosen in such a way that a minimum possible reference value

Smoothing wind power fluctuations by ANN-based pitch angle controller 281

enables generation of maximum possible power (1 p.u.). The minimum value is expected to be chosen as higher generator rotor speed is vulnerable to power system stability. For the specific wind turbine system adopted in SimPowerSystems (2010), this reference value has been chosen as 1.21 p.u. The error signal is sent to the PI pitch angle controller, which produces the command pitch angle (βc). It reduces the turbine operating efficiency to minimise power coefficient so that the generator maintains its control speed value.

Pitch servos are employed for proper positioning of the blades, which is modelled by using a first order delay system. The pitch actuation system cannot respond instantly. That is the reason why servo delay of 0.25 s has been chosen. The pitch actuation system is also limited by its actuation speed. A rate limiter for pitch rate of change of ±3 o/s has also been added to get more realistic responses from the pitch angle control system (Kamel et al., 2010). It gives the value of pitch angle (β).

4 ANN-based pitch angle controller

Wind generators may operate above rated wind speed or below rated wind speed (the rated wind speed in this study is 13.3 m/s). Two ANNs have been incorporated in the pitch angle controller for the operation of wind turbine above the rated wind incident (ANN-A) and below the rated wind incident (ANN-B).

ANNs are computing and processing systems based on the neural structure of the brain. It is made up of a large number of simple, highly interconnected processing elements that contend the structure and operation of the biological nervous system. However, a typical ANN has a simpler architecture than biological neurons. ANNs are typically organised in input, hidden and output layers. The communication between the input layer and one or more hidden layers are maintained through their weighted connections. Every neuron in the hidden layers connects all neurons in the output layer. The final results are obtained from the output layer according to the data processing in the hidden layer. Learning in ANNs is achieved through special training algorithms. These algorithms are based on the learning rules, presumed to mimic the learning mechanisms of biological systems (Yilmaz and Ozer, 2009).

ANNs differ in their architecture and the way they are trained. The selection of neural network includes the selection of number of layers, choice of transfer function, number of inputs and number of neurons in each layer. Alternative architectures should be tried to find the best performing structure on the trial and error basis. A four-layered ‘multi layer perceptron (MLP)’ network has been adopted in this paper with one input layer, two hidden layers and one output layer. Hidden layer helps in establishing correlations between input and output parameters and two hidden layers can model complex-mapping functions reasonably well. Each of input and output layers is represented by a single neuron and the number of neuron in each of the hidden layer is determined from the network optimisation process.

A tangent sigmoid nonlinear mapping function has been employed in this application, which helps in modelling functions of arbitrary shape. The number of neurons in the input layer and hidden layers are decided by experimentation, which involves training and testing different network configurations (Yurdusev et al., 2006). An architectural graph of an MLP network has been illustrated in Figure 3 (Haykin, 1994).

282 M.A. Chowdhury

Figure 3 Architectural graph of an MLP network with two hidden layers

A back propagation algorithm namely ‘Bayesian regulation’ has been used for training process in this paper, which updates weights according to the errors between network output and desired output. The algorithm cycles through the training as follows (Haykin, 1994):

Step 1 Initialisation of all weights at random.

Step 2 Calculation of the output vector ( )( )kpy according to

1( ) ( ) ( 1) ( 1) ( )

1

.

1,2, , ; 1, 2, ,

kNk k k k k

p p ip i ii

k

y tsgm W y θ

p N k M

−− −

=

⎡ ⎤= −⎢ ⎥

⎢ ⎥⎣ ⎦= =

∑… …

(11)

where ( 1)kipW − is the is the connection weight between the ith neuron in the (k –

1)th layer and pth neuron in the kth layer, ( )kpy the output of the pth neuron in the

kth layer, ( )kptsgm the tangent sigmoid activation function of the pth neuron in the

kth layer and ( )kpθ the threshold of the pth neuron in the kth layer.

Tangent sigmoid activation function ( )( )kptsgm is given as:

1 exp( )( )1 exp( )

xtsgm xx

− −=

+ − (12)

Step 3 Updating the weights according to

( 1) ( 1) ( ) ( 1)

1

( 1) ( )I

k k k knpip ip ni

n

W t W t yα δ− − −

−

+ = + ∑ (13)

where t is the iteration number, α the learning rate and ( )kpδ the local gradient of

the pth neuron in the kth layer.

Local gradient is given as

Smoothing wind power fluctuations by ANN-based pitch angle controller 283

1( ) ( ) ( 1) ( )

1

(.). ( )kN

k k k knp np nl pl

l

tsgm W tδ δ+

+

=

⎡ ⎤= ⎢ ⎥

⎢ ⎥⎣ ⎦∑ (14)

Step 4 Calculation of the total error (ε) according to 2( )

( ). .

1 1

MN MIM

n j n jn j

ε y y∧

= =

⎛ ⎞⎜ ⎟= −⎝ ⎠∑∑ (15)

Step 5 Iterate the calculation by returning to Step 2 until the total error is less than the desired error.

Figure 4 shows the proposed design of the two combined fuzzy logic controllers: ANN-A and ANN-B. Design strategies of ANN-A and ANN-B are described below.

Figure 4 Control scheme of the proposed ANN-based pitch angle controller

4.1 ANN-A

When the wind turbine operates during the wind incident above the rated value, the generator rotor speed exceeds the control speed value. In this mode of operation, the controller has got nothing to do regarding power fluctuation minimisation, because generator produces currents at its maximum rating irrespective of wind speed fluctuation, which ensures generation of constant wind power. The only concern of the controller is to shed mechanical power for preventing the generator rotor from going above the control speed limit value 1.21 p.u. The ANN-A has been proposed to incorporate the command pitch angle controller (βcA), which is only active in this mode of operation.

To obtain command pitch angle from the ANN-A (βcA), generator rotor speed variation from its reference value (eA) and its variation during a sampled time (δeA) are used as inputs. As control action of the ANN-A would be shedding mechanical power to limit the generator rotor speed to a control speed value, using generator rotor speed as the control input of the ANN-A is a means of direct control method. It gives more flexible control over the system by enabling constant observation on generator rotor speed.

The network is ready to be trained once the ANN structure is formed to check whether the structure is capable of producing the output variables from the inputs

284 M.A. Chowdhury

satisfactorily. Dynamic simulations are carried out to find out the required command pitch angle (βcA) for the inputs eA and δeA and thus 77 training samples are formed to carry out this process (Figure 5). Minimum error is the key indication of a better training performance. The structure of the network (number of layers and neurons), training and momentum coefficients were altered to minimise the error to find the best architecture. The training error is minimal when the number of neurons in the first and second hidden layers is 6 and 5 respectively.

Figure 5 Training data for ANN-A (see online version for colours)

4.2 ANN-B

On the other hand, when the wind turbine operates during the wind incident below the rated value, there is no generation of pitch angle. Any variation in wind speed can cause high fluctuations in wind power. To smooth the fluctuating wind power, two methods have been proposed to incorporate into the ANN-B controller by generating command pitch angle (βcB).

4.2.1 Method 1

The smoothing technique of method 1 is to determine a command output power (Pg_com) from the reference output power [the output power from the wind turbine with the conventional PI controller has been considered as reference output power (Pg_ref)] and generate pitch angle in such a way so that the generated output power can follow the command output power. To accomplish this,

• Command output power must be smooth.

• Smoothing is achieved by the generation of pitch angle with some drop in output power. As the generated output power is always lower than the reference output power, command output power must be ensured to be lower than the reference value so that the fuzzy rules for the generation of pitch angle are effectively set up by

Smoothing wind power fluctuations by ANN-based pitch angle controller 285

ensuring control input of variation of reference output power from the command output power in ANN-B falls in positive domain.

As the initial step, the smoothed version of reference output power, known as ‘initial EMA command output power (Pg_com")’ has been generated. This command value at any instant t is given as (NIST/SEMATECH, 2010):

_ 1 1(1 )g com t t tP C Pα α′′ = + − (16)

where C1 is the current value and P1 is the previous period’s value of the reference output power (Pg_ref) and α is the smoothing constant.

In this paper, 12 periods of average value (each of 1 s) is used in simulation. So, the EMA starts from 12 s when 12 period’s data are available. Smoothing constant (α) is chosen as 0.8, which indicates that 80% weight has been considered for the data of present period in comparison with 20% weight to the previous period’s data. The trend of weight assigned to the previous period’s data can be realised by expanding (16)

[ ]_ ' 1 1

21 1

1

10

(1 ) (1 )(1 ) (1 )

(1 ) (1 )

g com t t t t

t t t

ti t

t i ti

P C C PC C P

C P

α α α α

α α α α

α α α

− −

− −

−

− −

=

= + − + −

= + − + −

= − + −∑ (17)

The term (1 – α)t indicates that weights of the previous period’s data are exponentially decreasing. The distribution of the EMA weights for n samples is shown in Figure 6.

Figure 6 Distribution of EMA weights

The superiority of the EMA over other smoothing techniques is that the EMA can follow wind speed more rapidly because it uses its data of previous periods for next calculation (Kamel et al., 2010).

286 M.A. Chowdhury

To ensure the command output power (Pg_com) to be lower value than the reference value, a concept of correction factor (k) has been introduced, which is related to EMA command output power (Pg_com’) in the following manner.

_ _g com g comP k P′ ′′= × (18)

The correction factor (k) has been determined by ANN. Variation of reference output power from the command output power (eB1) and reference output power (Pg_ref) are used as inputs. 108 training samples are obtained from the dynamic simulations, which is given in Figure 7. ANN structure with 8 and 7 neurons have been selected for the first and second hidden layer, respectively for minimum training error.

Figure 7 Training data for determining correction factor (k) (see online version for colours)

During training, the ANN achieves an optimum weight matrix to evaluate the complex input-output relation. So, any small changes in the inputs results in alteration in the weight matrix, which are reflected in the output. This causes the trend of the signal to follow the reference value. So, the command signal is smoothed by EMA once again to achieve the final command value (Pg_com) in the following manner

_ 2 2(1 )g comP C Pα α= + − (19)

where C2 is the current value and P2 is the previous period’s value of the command value (Pg_com′).

Figure 8 shows that the command output power (Pg_com) achieved is lower than the reference output power (Pg_ref) over the whole 300 s period except for the period during the rapid change in operating points from the EMA command output power (Pg_com’) by the generation of correction factor (k), where the role of k is insignificant because output power simply switches to a different operating point and needs not to be smoothed. The role of k is not accountable during above rated wind incidents as well, because output power is generated at its maximum rating and needs not to be smoothed either.

Smoothing wind power fluctuations by ANN-based pitch angle controller 287

Figure 8 Obtaining command output power from EMA command output power by the generation of correction factor (k) (see online version for colours)

To obtain command pitch angle from ANN-B using method 1 (βcB1), variation of reference output power from the command output power (eB1) and its variation during a sampled time (δeB1) are used as inputs. 77 training samples are obtained from the dynamic simulations, which is given in Figure 9. ANN structure with 6 and 5 neurons have been selected for the first and second hidden layer respectively for minimum training error.

Figure 9 Training data for ANN-B (method 1) (see online version for colours)

288 M.A. Chowdhury

4.2.2 Method 2

Another method is proposed to minimise wind power fluctuations during below rated wind incidents for the FLS-B. The following steps have been carried out as a means of design strategy:

• Generated wind power (Pg) has been categorised into the stages with the steps of 0.05 p.u., which is defined as ‘power stage (PS)’ as shown in Table 2. The lower limit value of each corresponding PS is taken as a target value (Pg_tar) for the controller.

• If the generated wind power falls under a PS, a pitch angle would be actuated to shed mechanical power and limit the wind power to Pg_tar. For example, if Pg = 0.78 p.u. at any instant, a pitch angle is actuated to make Pg=0.75 p.u. (see PS3 in Table 2).

Table 2 Power stages

Power stage Range Pg_tar PS1 0.9 < Pg ≤ 0.85 0.85 PS2 0.85 < Pg ≤ 0.8 0.8 PS3 0.8 < Pg ≤ 0.75 0.75

.

.

. PS16 0.15 < Pg ≤ 0.1 0.1 PS17 0.1 < Pg ≤ 0.05 0.05 PS18 0.05 < Pg ≤ 0 0

Figure 10 Training data for ANN-B (method 2) (see online version for colours)

To obtain command pitch angle from ANN-B using method 2 (βcB2), variation of reference output power from the target value selected by ANN-B (eB2) and its variation during a sampled time (δeB2) are used as inputs. 77 training samples are obtained from the

Smoothing wind power fluctuations by ANN-based pitch angle controller 289

dynamic simulations, which is given in Figure 10. ANN structure with 6 and 5 neurons have been selected for the first and second hidden layer respectively for minimum training error.

Control action of ANN-B would be limiting output power to a certain target value and output power has been divided into stages instead of generator rotor speeds, which make the control design easier because the use of the rotor speed would cause nonlinear.

The range of the command pitch angle output is different for each corresponding PS. It generally requires more pitch angle generation as wind power falls in PS of lower levels. That is why; the output is multiplied with a multiplication factor (n), which is of higher value when the output power resides in lower PS. The values of n for different PS are listed in Table 3.

Table 3 Multiplication factor (n) for method 2

Power stage n Power stage n

PS1 0 PS12 2.33

PS2 1 PS13 2.667

PS3 1.167 PS14 3

PS4–PS7 1.5 PS15 3.33

PS8–PS9 1.83 PS16 10

PS10 2 PS17 41.667

PS11 2.167 PS18 75

5 Simulations results

To investigate the effectiveness of the proposed methods, a wind turbine connected to a grid has been simulated using MATLAB/Simulink. The control action and collective responses of the conventional method (with PI pitch angle controller) and the proposed methods (with ANN-based pitch angle controller) at the grid have been compared. A fluctuating wind is simulated, whose pattern is adopted from Kamel et al. (2010) and Gracia et al. (2008). The effectiveness of the proposed fuzzy logic pitch angle controller is demonstrated in the following sections.

5.1 Evaluation of ANN-A

The conventional PI pitch angle controller activates pitch actuation [Figure 11(b)] only when the above rated wind incidents prevail (between 102 s and 160.2 s). In this mode, the proposed controller also activates pitch actuation for shedding mechanical power to prevent generator rotor from going above the control speed limit value by producing the current at its maximum rating and maintains constant output power [Figures 11(c) and 11(d)] regardless of the abrupt change of wind speed.

290 M.A. Chowdhury

Figure 11 Evaluation of the proposed methods, (a) wind speed, (b) pitch angle, (c) active power and (d) reactive power

(a)

(b)

(c)

Smoothing wind power fluctuations by ANN-based pitch angle controller 291

Figure 11 Evaluation of the proposed methods, (a) wind speed, (b) pitch angle, (c) active power and (d) reactive power (continued)

(d)

5.2 Evaluation of ANN-B

During below rated wind incidents (for the first 102 s and the last 139.8 s), there is no pitch angle generation by the conventional PI pitch angle controller [Figure 11(b)] as there is no point of limiting the generator rotor speed, which ensures the wind turbine operation at its maximum possible efficiency. But, the outputs of active and reactive powers have high fluctuations due to abrupt variations in wind speed [Figures 11(c) and 11(d)]. This is because wind power depends on the cube of the wind speed and the input torque can not be controlled (Gracia et al., 2008). The SOPF is achieved by the proposed methods and evaluated in the following sections.

5.2.1 Evaluation of the proposed method 1

For the control strategy in the proposed method 1, a command value is generated smaller than the reference output power with the proper selection of correction factor by fuzzy reasoning. The controller actuates pitch to limit the output power so that the output power follows the command value. It means power fluctuation minimisation is achievable with the cost of some output power drops. Figure 11(c) shows that partial smoothing of output power is achieved during below rated wind incidents. In Figure 11(d), negative value of the reactive power refers to absorption of reactive power by the induction generator from power grid (due to having capacitive loads), which is directly related to the active power generation and thus the absorbed reactive power is partially smoothed with the generation of partially smoothing active power. The generation of pitch angle for method 1 is shown in Figure 11(b).

5.2.2 Evaluation of the proposed method 2

The proposed method 2 causes pitch actuation to limit the output power to a target value (Pg_tar) during below rated wind incidents. In Figure 11(c), it can be seen that the proposed pitch angle controller sheds the output active power to ensure smoothing output power of 0.7 p.u., as it falls into PS4 when there is no pitch angle generation for the first

292 M.A. Chowdhury

40.5 s. We get smoothing output power: 0.65 p.u in the next 57.6 s, 0.8 p.u. between 161.4 s and 218.4 s and 0.6 p.u. onwards. The active power is always shed to Pg_tar corresponding to the power stage it resides for smoothing the fluctuations. The reactive power absorbed by the generator is also automatically smoothed with the generation of smoothing active power [Figure 11(d)]. The generation of pitch angle for method 2 is shown in Figure 11(b).

5.3 Numerical validation of the proposed methods

The validity of the proposed methods in output power smoothing has been carried out numerically by introducing the concept of maximum energy function (Pmax) and smoothing function (Psmooth), which are expressed as (Senjyu et al., 2006):

max0

( )t

gP P t dt= ∫ (20)

( )0

t gsmooth

dP tP dt

dt= ∫ (21)

As compared with the conventional method, maximum energy functions for both proposed methods drop slightly because the pitch angle remains fixed at 0o when conventional PI pitch angle controller is applied. Since the purpose of this work is to smooth the output power, a drop in the output power can not be avoided. But, this drop should be as minimum as possible for efficient operation. Figure 12(a) shows that a drop in output power is approximately equal to 2.94% and 5.53% for method 1 and 2, respectively, which is a significant improvement as compared to the conventional smoothing method with approximate 30% drop in output power (Senjyu et al., 2006).

Figure 12 Numerical validation of the proposed fuzzy logic controllers, (a) maximum energy function and (b) smoothing function

(a)

Smoothing wind power fluctuations by ANN-based pitch angle controller 293

Figure 12 Numerical validation of the proposed fuzzy logic controllers, (a) maximum energy function and (b) smoothing function (continued)

(b)

On the other hand, if smoothing function (Psmooth) in (21) is small, the output power fluctuation is small, which is the indication of good performance in smoothing output power. Figure 12(b) shows that smoothing function drops to about 2/3 and 1/2 due to the application of the proposed methods 1 and 2, respectively, in the comparison with the conventional method.

The proposed method 1 demonstrates the economic benefit by employing power storage system of smaller capacity besides the controller for partial smoothing purpose. The proposed method 2 ensures nearly complete smoothing with a little more drop in output power than the proposed method 1. It demonstrates a very little requirement of compensation of output power fluctuations by means of power storage system during normal operations ensuring greater economic benefits.

6 Conclusions

This paper presents an ANN-based pitch angle controller, which is controlled by two mutually exclusive neural networks. The main functions of the controller are to regulate output power during above rated wind incidents and smooth wind power fluctuations during below rated wind incidents by dynamic pitch actuation. Two methods have been depicted for the smoothing of wind power fluctuations The first method is to determine the command output power based on the EMA with a proper selection of correction factor by neural network to make output power to follow that command value. The second method is to select the target output power values dynamically according to the wind incident and limit power to obtain the target output. The performances of the proposed ANN-based pitch angle controller with both methods have been compared with that of the conventional PI pitch angle controller. The results indicate that the proposed methods smooth output power fluctuations with significantly small drop of output power as compared to the previous works. The method 1 performs partial smoothing with only 2.94% drop in output power demonstrating the economic benefit by employing power storage system of smaller capacity besides the controller for smoothing purpose. The

294 M.A. Chowdhury

method 2 performs nearly complete smoothing with 5.53% drop in output power demonstrating greater economic benefits with a very little requirement of compensation of output power fluctuations by means of power storage system during normal operations.

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